Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia

Juvenile myelomonocytic leukemia (JMML), a clonal hematologic malignancy, originates from mutated hematopoietic stem cells (HSCs). The mechanism sustaining the persistence of mutant stem cells, leading to leukemia development, remains elusive. In this study, we conducted comprehensive examination of gene expression profiles, transcriptional factor regulons, and cell compositions/interactions throughout various stages of tumor cell development in Ptpn11 mutation-associated JMML. Our analyses revealed that leukemia-initiating Ptpn11E76K/+ mutant stem cells exhibited de novo activation of the myeloid transcriptional program and aberrant developmental trajectories. These mutant stem cells displayed significantly elevated expression of innate immunity-associated anti-microbial peptides and pro-inflammatory proteins, particularly S100a9 and S100a8. Biological experiments confirmed that S100a9/S100a8 conferred a selective advantage to the leukemia-initiating cells through autocrine effects and facilitated immune evasion by recruiting and promoting immune suppressive myeloid-derived suppressor cells (MDSCs) in the microenvironment. Importantly, pharmacological inhibition of S100a9/S100a8 signaling effectively impeded leukemia development from Ptpn11E76K/+ mutant stem cells. These findings collectively suggest that JMML tumor-initiating cells exploit evolutionarily conserved innate immune and inflammatory mechanisms to establish clonal dominance.


INTRODUCTION
Juvenile myelomonocytic leukemia (JMML), a pediatric myeloproliferative neoplasm, manifests as a clonal hematopoietic disorder characterized by the excessive production of myeloid cells.This disease originates from driver mutations acquired in hematopoietic stem cells (HSCs) and is propagated and sustained by these mutated stem cells, known as leukemia-initiating cells [1][2][3][4] .JMML has limited therapeutic options.Relapse remains the primary cause of treatment failure, most likely due to the persistence of therapy-resistant, self-renewing leukemia-initiating cells [1][2][3][4] .Addressing this issue is crucial for improving treatment outcomes in JMML patients.
2][3][4] .These mutations play a causal role in driving JMML development [5][6][7][8] .JMML arises from an HSC harboring a genetic mutation, yet the mechanisms by which the initially mutated stem cell (leukemia-initiating cell) acquires a competitive advantage and evades immune surveillance remain unexplored.Additionally, the speci c reasons behind the propensity of disease-associated mutations to induce myeloid malignancy are not fully understood, and the molecular mechanisms governing the aberrant repopulation of these leukemia-initiating stem cells remain elusive.Understanding these mechanisms could illuminate strategies for therapeutically targeting and eliminating JMML initiating stem cells in established disease.
Of the genetic lesions identi ed in JMML, the protein tyrosine phosphatase PTPN11 (SHP-2), a positive regulator of RAS signaling 9,10 , is the most frequently mutated (heterozygous) 11,12 .Mutations in PTPN11 lead to a signi cant increase in the catalytic activity of SHP-2 12,13 .Patients carrying PTPN11 activating mutations have the worst prognosis among all subtypes of JMML [14][15][16][17] .To elucidate the mechanisms underlying the pathogenesis of PTPN11-mutated JMML, our laboratory created a conditional Ptpn11 allele in mice with the Ptpn11 E76K mutation, the most common PTPN11 mutation found in JMML 11,12 , and developed an inducible disease model 6,18 .Induction of the Ptpn11 E76K mutation in the hematopoietic system resulted in a JMML-like myeloproliferative neoplasm with complete penetrance, a rming the causative role of this mutation in JMML 6 .In the present study, we take advantage of this unique disease model to investigate the cellular and molecular mechanisms involved in the pathological process of JMML following induction of the disease mutation.Our ndings from single-cell transcriptomic pro ling and experimental validations reveal an aberrant activation of innate immune responses in the mutated stem cells.These leukemia-initiating cells exploit innate immune and in ammatory mechanisms to gain a competitive advantage and evade anti-tumor immunity, ultimately leading to clonal dominance.

RESULTS
Aberrant activation of innate immune and in ammatory responses in leukemia-initiating Ptpn11 E76K/+ stem cells.
To explore the intricate mechanisms of JMML pathogenesis, we conducted a comprehensive single-cell RNA sequencing (scRNA-seq) analysis on bone marrow (BM) cells isolated from mice with induced JMML (Ptpn11 E76K/+ /Mx1-Cre) 6 and wild-type (WT, Ptpn11 +/+ /Mx1-Cre) control littermates.Utilizing gene expression pattern-based cell clustering, we identi ed 11 distinct cell clusters within the BM population on a t-distributed stochastic neighbor embedding (t-SNE) plot (Extended Data Fig. 1A).Clear distinctions among these clusters were evident in the heatmap representation of the expression patterns of the top 10 differentially expressed genes (DEGs) in each cluster (Extended Data Fig. 1B).Leveraging reference datasets 19,20 permitted the identi cation of various hematopoietic cell types in different developmental stages, including HSCs, granulocyte-macrophage progenitors (GMPs), megakaryocyticerythroid progenitors (MEPs), monocytes, neutrophils, T cells, B cells, and others (Extended Data Fig. 1C).Cell type-speci c signature genes were indeed well-represented in the identi ed cell clusters (Extended Data Fig. 1D).Notably, Ptpn11 E76K/+ mutant HSCs (leukemia-initiating cells) and GMPs exhibited reduced abundance, while monocytes and neutrophils displayed an increase compared to their WT (Ptpn11 +/+ ) counterparts (Extended Data Fig. 1C).The reduction of mutant stem cells/progenitors and the myeloid shift in hematopoietic cell development indicated hyperactivation of these leukemiainitiating cells and myeloid-committed progenitors.The decreased numbers of T cells and B cells in their hematopoietic systems suggested that the enhanced myeloid cell production resulted from skewed differentiation of Ptpn11-mutated stem cells.Gene set enrichment analysis (GSEA) demonstrated the upregulation of genes associated with immune processes and chemokine activities, particularly through the CC chemokine receptor (CCR), in Ptpn11 E76K/+ mutant hematopoietic cells (Extended Data Fig. 1E).
Gene expression pro le-based cell clustering of the stem cell population revealed two distinct clusters equivalent to long-term HSCs (LT-HSCs) and short-term HSCs (ST-HSCs) according to the reference datasets 20 .The percentage of LT-HSCs decreased while the percentage of ST-HSCs increased in Ptpn11 E76K/+ mice compared to those in WT littermates (Fig. 1A).In our analyses we also observed that among the top 20 DEGs in LT-HSCs compared to ST-HSCs, several genes were highly expressed only in LT-HSCs (Fig. 1B).In particular, Sdpr was predominantly expressed in LT-HSCs, indicating its potential as a distinctive marker for distinguishing LT-HSCs from ST-LT-HSCs.Notably, 177 genes in total were signi cantly differentially expressed in Ptpn11 E76K/+ LT-HSCs versus WT LT-HSCs (Fig. 1C).The Gene Ontology (GO) enrichment analysis of these DEGs highlighted the predominant elevation of defense reactions to bacterial infection, innate immune response, Toll-like receptor 4 (TLR4) signaling, and in ammation-associated pathways (Fig. 1D).Consistent with the hyperactivation of Ptpn11 E76K/+ HSCs, GSEA demonstrated a decrease in the expression of stem cell/progenitor-associated genes and upregulated/downregulated genes in HSCs versus GMPs in Ptpn11 E76K/+ HSCs (Fig. 1E), suggesting a loss of stemness and priming towards the myeloid lineage in Ptpn11-mutated HSCs.Similarly, 173 DEGs were identi ed in Ptpn11 E76K/+ ST-HSCs compared to WT ST-HSCs (Extended Data Fig. 2A), with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicating dysregulation of anti-viral immune response pathways, ribosome biogenesis, and spliceosome function.(Extended Data Fig. 2B).
Moreover, several cell surface molecules were differentially expressed in Ptpn11 E76K/+ LT-HSCs.Among the most signi cant DEGs, Cd52 and Cd9 were upregulated, while transcriptional expression of the early stem/progenitor cell marker Cd34 was diminished (Fig. 1H).In addition, Cd33, P2ry14/Gpr105, and Gpr150 showed a marked upregulation in Ptpn11 E76K/+ LT-HSCs.These unique expression patterns of cell surface molecules in Ptpn11 mutant LT-HSCs hold promise for their utilization as therapeutic targets or biomarkers for JMML stem cells.Furthermore, Rage/Ager, the receptor for the S100a9/S100a8 heterodimer (calprotectin) 21 typically expressed on myeloid immune cells exhibited substantial upregulation in Ptpn11 E76K/+ LT-HSCs, indicating potential autocrine feedback activities in these leukemia-initiating cells.Given that S100a9 expression was signi cantly upregulated in Ptpn11 E76K/+ LT-HSCs (Extended Data Fig. 3A), we sought to identify transcriptional factors potentially associated with this upregulation.To this end, we conducted Venn diagram data analysis involving the 177 DEGs in Ptpn11 E76K/+ LT-HSCs and 58 transcriptional factors related to S100a9.This analysis revealed Spi1 and Smarca4 (Extended Data Fig. 3B).Of the 47 dysregulated transcriptional factors in Ptpn11 E76K/+ LT-HSCs, Spi1 showed a signi cant upregulation, whereas Smarca4 was downregulated (Extended Data Fig. 3C), suggesting that the elevated levels of Spi1 may have contributed to the observed overexpression of S100a9.
Profound impact on the myeloid lineage by the Ptpn11 E76K mutation.
The in uence of the Ptpn11 E76K mutation extended beyond the stem cell population, signi cantly affecting myeloid-committed GMPs.Gene expression pro ling identi ed 4 distinct cell clusters in GMPs, revealing heterogeneity among these progenitors (Fig. 2A).Interestingly, Ptpn11 E76K/+ GMPs exhibited a notable shift in cell composition, with Cluster 3 emerging as a unique and overrepresented subpopulation, constituting approximately 60% of the total.The heatmap representation of the top 10 DEGs in each cell cluster highlighted clear differences among these clusters, with Cluster 1 enriched in Prom1, Clu, Mgam, Gpx3, and Slco4c1, and Cluster 3 marked by high expression of Fbp1, Tmem53, Cracr2b, and Dlg2 (Fig. 2B).Overall, 127 genes were signi cantly differentially expressed in Ptpn11 E76K/+ GMPs compared to their WT counterparts (Fig. 2C).GO enrichment analysis of the DEGs underscored enrichment in innate immune and in ammatory pathways in Ptpn11 mutant GMPs (Fig. 2D).This included pathways related to the positive regulation of immune response, neutrophil activation, neutrophil-mediated killing of bacteria, defense response to bacteria, and innate immune response.GSEA revealed an enrichment of genes typically associated with later-stage progenitors, such as monocyte and dendritic cell progenitors, and neutrophil progenitors in Ptpn11 E76K/+ GMPs relative to WT GMPs (Fig. 2E), indicative of enhanced differentiation activities in these mutant GMPs.Cluster 3, representing the major subpopulation within Ptpn11 E76K/+ GMPs, displayed high and unique expression of Arl11, Fbp1, Slc31a2, Hnmt, Tmem53, Cracr2b, among others (Extended Data Fig. 4A).Differential gene expression analysis between Cluster 3 and Cluster 1, the major population in WT GMPs, revealed 114 genes with distinct expression patterns (Extended Data Fig. 4B).KEGG pathway analyses illustrated the upregulation of genes involved in autoimmune responses, bacterial infection responses, natural killer cell-mediated cytotoxicity, neutrophil extracellular trap formation, and ribosome, whereas downregulated pathways included phagosome, ribosome, RNA transport, spliceosome, RNA degradation, oxidative phosphorylation, and thermogenesis pathways in Ptpn11 E76K/+ GMPs (Extended Data Fig. 4C).
To explore whether the transcriptional landscape changes in Ptpn11 E76K/+ cells across different developmental stages shared commonality, the top 50 signi cant DEGs in Ptpn11 E76K/+ and WT stem cells, GMPs, monocytes, and neutrophils are shown in Fig. 4A.Venn diagram data analysis for DEGs in the different cell populations identi ed 44 co-events (Fig. 4B).Remarkably, these genes were consistently upregulated or downregulated in Ptpn11 E76K/+ cells throughout all developmental stages, without any exceptions (Fig. 4C).This observation implies that they were cell-intrinsically dysregulated by the Ptpn11 E76K mutation.Many of these co-DEGs were associated with innate immune signaling and in ammatory pathways, including S100a11, Retnlg, and Lyz2.Interestingly, genes involved in ribosomal biogenesis, such as Rplp0, Rps3, and Rpl21 were upregulated, while Rpl41, Rpl37a, Rps28, Rpl38, Rpl23a, and Rps15 were repressed.Dysregulation of ribosomal biogenesis and function can collectively contribute to cellular abnormalities, genomic instability, and the development of malignancies 22,23 .
These ndings underscore that the impact on ribosomal function is a common pathological effect of the Ptpn11 mutation across different cell types.
Altered developmental trajectories and cell-cell communications in leukemia-initiating Ptpn11 E76K/+ stem cells.
Branched expression analysis modeling (BEAM), followed by hierarchical clustering analysis, identi ed three distinct gene expression modules during the differentiation process from stem cells to monocytes and neutrophils.Notable differences in the dynamic changes in the expression of genes enriched in all modules were observed in the differentiation process of Ptpn11 E76K/+ stem cells (Fig. 5A).A markedly higher number of genes showed dynamic changes in expression within Module 2, whereas fewer genes demonstrated such changes in Module 3 in the context of the Ptpn11 mutant cellular processes.Pseudotime mapping analysis, which infers the developmental trajectory or temporal progression of cells within a heterogeneous population based on gene expression pro les, revealed that leukemiainitiating Ptpn11 E76K/+ mutant stem cells gave rise to GMPs mainly in one direction as opposed to two in WT counterparts (Fig. 5B, upper row), suggesting the impact on the mutation of GMPs.While the inferred pseudotime of neutrophil development from Ptpn11 E76K/+ stem cells remained relatively unchanged, two diverging cell fates were observed during the differentiation of these leukemia-initiating cells towards monocytes, contrasting with the essentially singular fate observed in the WT control, and the inferred pseudotime of monocyte development from the leukemia-initiating cells was prolonged (Fig. 5B, upper row).In addition, intermediate monocytes in a transitioning state were increased in the Ptpn11 E76K/+ group, suggesting a delay or arrest in their differentiation and maturation.Further analyses focusing on speci c cell compartments showed a slight difference in the diffusion trajectories within GMPs between Ptpn11 E76K/+ and WT counterparts (Fig. 5B, lower row).No notable differences in Ptpn11 E76K/+ neutrophil diffusion maps were detected, indicating relatively normal differentiation and maturation within these two cell populations.In contrast, Ptpn11 E76K/+ monocytes exhibited two distinct developmental paths compared to the single direction observed in WT monocytes (Fig. 5B, low row), implying the generation of various subpopulations in Ptpn11 E76K/+ monocytes along distinct developmental routes.
Cell-cell communication analyses based on the expression of ligands and their cognate receptors revealed enhanced interactions between neutrophils and stem cells in Ptpn11 E76K/+ mice compared to those in WT littermates (Extended Data Fig. 5A and 5B).Furthermore, interactions among Ptpn11 E76K/+ stem cells were increased relative to those in WT stem cells (Extended Data Fig. 5A and 5B).A closer examination of neutrophil-stem cell communications indicated that interactions mediated by IL-1β, TGFβ, and Oncostatin M were enhanced in Ptpn11 E76K/+ mice compared to those in WT mice (Extended Data Fig. 5C), providing additional evidence that leukemia-initiating Ptpn11-mutated stem cells were situated in an in ammatory microenvironment.
Leukemia-initiating Ptpn11 E76K/+ stem cells are primed by the myeloid transcriptional program.
Cell identity and functional speci city are collectively governed by transcription factors and the expression levels of their target genes.The overall transcriptional activities in Ptpn11 E76K/+ stem cells were elevated compared to those in their WT counterparts (Extended Data Fig. 6), consistent with more active cellular processes in leukemia-initiating Ptpn11-mutated stem cells.To further elucidate the mechanisms through which the Ptpn11 E76K mutation in uences cell behavior, we conducted single cell regulatory network inference and clustering (SCENIC) analysis (transcriptional factor regulon analysis).
The activities of many transcription factors in Ptpn11 E76K/+ stem cells, GMPs, monocytes, and neutrophils were altered compared to those in their WT counterparts, as indicated by regulon activity scores.In Ptpn11 E76K/+ stem cells, the transcriptional activities of Atf3, Egr1, Jun, Jund, Klf6, Fos, and Gata2 were signi cantly decreased, while those of Irf7, Irf8, Maf, and Myc were increased (Fig. 6A).Importantly, regulon speci city scores (RSS), re ecting the association between regulon activities and cellular speci city, revealed that among these differentially functioning transcription factors, the myeloid transcription factors Ets1, Cebpe, and Nfe2 were highly associated with the identity speci city of Ptpn11 E76K/+ stem cells, as opposed to Tcf7l2, Relb, and Irf5 for WT HSCs.At the GMP level, the activities of myeloid-speci c transcription factors Cebpe, Cebpb, and Ets1 were markedly increased in Ptpn11 E76K/+ GMPs, and their cellular speci city was determined by Cebpe, Ets1, and Myc compared to Cebpe, E2f1, and Klf6 in WT GMPs (Fig. 6B).Activities of transcription factors Irf7, Cebpb, Fos, Irf5, Irf8, Klf4, and Maf were signi cantly higher in Ptpn11 E76K/+ monocytes than those in WT cells, and the identity speci city of Ptpn11 mutant monocytes was highly associated with transcription factors Irf7, Mafg, and Irf8 according to RSS (Fig. 6C).Similarly, the distinction in transcriptional factor determinants in uencing the speci city of Ptpn11 E76K/+ neutrophils (Mafg, Cebpb, and Junb) compared to those governing WT neutrophils (Maf, Irf8, and Junb) was also apparent (Fig. 6D).
Consistent with the regulon results, Ptpn11 E76K/+ stem cells and GMPs demonstrated heightened cell cycling, as evidenced by the loss of quiescence (the G 0 phase in the cell cycle) and an increased number of cells in the G 2 /M phase, based on single-cell transcriptomes and a reported predictor for allocating individual cells to G 0 , G 1 /S, and G 2 /M cell cycle phases 24 (Fig. 7A).The cell division/replication-related histone H1 family members (Hist1h1c, Hist1h1d, Hist1h1e, and Hist1h2ae) and CDK1 were upregulated in both Ptpn11 E76K/+ stem cells and GMPs (Fig. 7B).Additionally, GSEA revealed a signi cant enrichment of cell cycling-associated gene sets in Ptpn11 E76K/+ stem cells (Fig. 7C).Both Ptpn11 E76K/+ stem cells and GMPs exhibited a high enrichment of GM-CSF response gene sets.This observation aligns with the well-established high sensitivity of JMML cells to GM-CSF 25,26 .S100a9 and S100a8, aberrantly expressed in Ptpn11 E76K/+ stem cells, contribute signi cantly to leukemogenesis.
Given the prominent upregulation of S100a9 and S100a8 in Ptpn11 E76K/+ mutant long-term stem cells (Fig. 1G) and their diverse roles in various cell types 27,28 , we investigated their potential role in these tumor initiating cells.First, we con rmed a signi cant increase (> 8-fold) in the expression levels of S100a9 and S100a8 in mutant stem cells isolated from Ptpn11 E76K/+ mice compared to those in WT HSCs (Fig. 8A).Importantly, expression levels of S100a9 and S100a8 were also elevated approximately 7-fold in leukemic stem/progenitor cells (CD34 + ) from PTPN11-mutated JMML patients compared to those in normal CD34 + hematopoietic stem/progenitors (Fig. 8B).The overexpression of S100a9 and S100a8 by Ptpn11 E76K/+ mutant stem cells appeared to promote the growth of these leukemia-initiating cells.Ptpn11 E76K/+ stem cells cultured in ex vivo expansion medium exhibited signi cantly accelerated proliferation compared to WT HSCs.However, this growth advantage was mitigated by tasquinimod, an inhibitor of S100a9/S100a8 that disrupts their interactions with receptors RAGE and TLR4 29,30 (Fig. 8C), which were also highly expressed on these cells (Fig. 1H).Additionally, the elevated differentiation capabilities of Ptpn11 E76K/+ mutant stem cells to form myeloid colonies compared to those of WT HSCs were substantially decreased by tasquinimod (Fig. 8D).These ndings suggest that S100a9 and S100a8 signi cantly contribute to the clonal expansion and enhanced myeloid differentiation of leukemiainitiating Ptpn11-mutated stem cells through autocrine effects.
Previous studies have proposed a signi cant role for S100a9 and S100a8 expressed in tumor cells in recruiting MDSCs, which are known for their association with immunosuppression and in ammation 27,31,32   .These heterogeneous cells co-express CD11b, Ly6G, and Ly6C myeloid lineage markers To test this possibility and further determine the role of S100a9 and S100a8 in the leukemogenic activities of Ptpn11 E76K/+ stem cells in an in vivo setting, we evaluated the therapeutic impact of the S100a9/S100a8 inhibitor tasquinimod using a widely used transplantation leukemia model.Ptpn11 E76K/+ /Mx1-Cre/mTmG mice were generated by crossbreeding of Ptpn11 E76K/+ /Mx1-Cre mice 6 with lineage tracing mTmG transgenic mice 33 , which expressed red uorescent protein (RFP) but transitioned to green uorescent protein (GFP) upon the induction of Cre expression (and the Ptpn11 E76K mutation).To mimic clinical scenarios, we combined BM cells from Ptpn11 E76K/+ /Mx1-Cre/mTmG leukemic mice with WT BM cells from congenic BoyJ mice at a 10:1 ratio and transplanted mixed cells into lethally-irradiated BoyJ mice.Four weeks post-transplantation, when donor cells were engrafted, tasquinimod or vehicle was administered to mice via drinking water for 4 weeks (Fig. 8F).Despite the high ratio of leukemic cells in the mixed donor cells, the reconstitution of leukemic cells from Ptpn11 E76K/+ mutant stem cells in the recipient mice was approximately 50% due to the hyperactivation and signi cant depletion of the mutant stem cell population (known as exhaustion) in the BM collected from the leukemic mice 6 .Importantly, in response to tasquinimod treatment, a notable reduction in total leukemic cells (GFP + ) in the peripheral blood (PB) was observed (Fig. 8G).Myeloid cells (Mac-1 + ) in the GFP + leukemic cell compartment (Fig. 8H) and the entire PB (Fig. 8I) signi cantly decreased, indicating that the skewed myeloid differentiation of leukemia-initiating Ptpn11 E76K/+ stem cells was largely recti ed by blocking S100a9/S100a8 function.
Mice were euthanized after 4 weeks of treatment.White blood cell counts (WBCs) in the tasquinimodtreated group signi cantly decreased, speci cally in neutrophils and monocytes, with no apparent changes in red blood cell counts (RBCs) (Fig. 8J).Splenomegaly was also ameliorated in tasquinimodtreated mice (Fig. 8K).Total leukemic cells (GFP + ) in the spleen, Mac-1 + myeloid cells in the GFP + leukemic compartment and the entire spleen all decreased (Fig. 8L).Similar therapeutic effects were also observed in the BM (Fig. 8M).Furthermore, we assessed the impact of the S100a9/S100a8 inhibitor on leukemia-initiating mutant stem cells.As shown in Fig. 8N, GFP + Ptpn11 E76K/+ mutant stem cells in the BM and early leukemic progenitor cells (Lineage − Sca-1 + c-Kit + ) in the spleen signi cantly decreased in the inhibitor-treated mice.Consistently, the cell cycling of hyperactive Ptpn11 E76K/+ stem cells was reduced by the treatment (Fig. 8O).Moreover, apoptosis in these mutant stem cells increased in the inhibitor-treated mice (Fig. 8P), suggesting that S100a9 and S100a8 played an important role for the survival of these leukemia initiating cells.Finally, we visualized Ptpn11 E76K/+ stem cells and surrounding cells in tasquinimod-or vehicle-treated mice and found that the distance between these leukemiainitiating cells (CD150 + CD11b − Ly6G − CD3 − B220 − Ter119 − CD48 − ) (cyan) and the closest PMN-MDSCs (CD11b + Ly6G + ) (yellow) was signi cantly increased following tasquinimod treatment (Fig. 8Q), con rming that the recruitment of PMN-MDSCs to the microenvironment of Ptpn11 mutant stem cells was attributed to S100a9/S100a8 overexpressed by these leukemia-initiating cells.

DISCUSSION
While considerable progress has been made in understanding the etiology of JMML, numerous questions remain, particularly concerning the cellular and molecular mechanisms that confer a selective advantage to the original leukemia-initiating cells.Understanding these mechanisms can illuminate how leukemia-initiating cells persist in established disease and how these tumor precursor cells may be effectively targeted and eliminated therapeutically.By undertaking a comprehensive characterization of the transcriptomic landscapes across all stages of tumor cell development in Ptpn11 mutationassociated JMML and substantiating our ndings through experimental validation, we have discovered that Ptpn11-mutated stem cells (leukemia-initiating cells) are primed by the myeloid transcriptional program and that innate immune and in ammatory responses are aberrantly activated in these cells.These mutant stem cells exhibit strikingly heightened expression of evolutionarily conserved genes that are typically activated in mature myeloid cells during pathogen defense, including anti-microbial peptides (Camp, Lcn2, Lyz2, Ltf, Chil3, and Pglyrp1) and essential trace metal-sequestering proteins (S100a9 and S100a8), which also function as pro-in ammatory proteins triggering and amplifying innate immune responses 27,28 .The innate immune system is conventionally activated through the recognition of pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) proteins derived from host cells or damaged cells by pattern-recognition receptors, including TLRs, on myeloid immune cells.These patterns play an important role in recruiting and activating myeloid immune cells, initiating in ammation to eliminate invading microorganisms 27,28 .S100a9 and S100a8, which show the most signi cant overexpression in Ptpn11 E76K/+ mutant stem cells, are also categorized as DAMPs.They preferentially heterodimerize to form calprotectin, which, like their monomeric/homodimeric forms, are endogenous ligands for TLR4, Rage/Ager, and CD33 21,34,35 on myeloid effector cells, activating intracellular signaling pathways and culminating in the production of in ammatory cytokines, chemokines, and antimicrobial peptides.Interestingly, the expression of Rage/Ager and CD33 is also markedly elevated on Ptpn11 mutant stem cells, producing autocrine effects.The autocrine effects of S100a9/S100a8 indeed contributed to the expansion of these leukemia initiating cells (Fig. 8C).Moreover, given the well-characterized detrimental effects of in ammatory challenges on normal HSCs 36,37 , the pro-tumoral in ammatory milieu provides leukemia-initiating mutant stem cells with a competitive advantage over normal counterparts, ultimately resulting in their clonal dominance.
S100a9 and S100a8 may also contribute to immune evasion of JMML-initiating mutant stem cells by chemoattracting and expanding immunosuppressive MDSCs in the microenvironment.MDSCs are classically linked to immunosuppression, in ammation, and cancer, profoundly inhibiting T cell-and NK cell-mediated antitumor immunity through various mechanisms 27,31,32 .S100a9 is crucial for MDSC recruitment as MDSC accumulation in tumors is abolished in S100a9-null mice 38 , and expression of S100a9 in transgenic mice drives expansion and activation of MDSCs 35 .These immune suppressive cells can also secrete abundant S100a9/S100a8 heterodimers, bind to their own surface receptors and nurture an autocrine feedback loop that sustains MDSC recruitment, thereby maintaining immune suppression within the local microenvironment 21 .Moreover, S100a9 also contributes to anti-tumor immunity by inhibiting dendritic cell differentiation 38 .Ptpn11 E76K/+ mutant stem cells indeed demonstrate a strong ability to attract MDSCs, and this chemoattracting effect is diminished by the inhibitor of S100a9/S100a8 (Fig. 8E and 8Q).Furthermore, administration of the S100a9/S100a8 inhibitor impedes leukemia development from Ptpn11 E76K/+ mutant stem cells (Fig. 8F-8P).These results strongly suggest that the overexpression of S100a9 and S100a8 by Ptpn11mutated stem cells plays a pivotal role in the initial leukemogenic process.
Further investigations are necessary to elucidate how the Ptpn11 E76K mutation instigates a myeloidspeci c transcriptional program and co-opts innate immune responses in the mutated stem cells.Shp-2 (encoded by Ptpn11) is predominantly localized to the cytosol and plays a prominent positive role in Ras signaling 9,10 .Since other genes that are mutated in JMML are also clustered in the Ras signaling pathway, it is conceivable that the Ptpn11 mutation causes pathogenic effects mainly through the Ras pathway.However, Shp-2 is also localized to the nucleus and the mitochondrion [39][40][41] .There is therefore a possibility that the Ptpn11 E76K mutation in uences myeloid-speci c transcriptomic activities through its nuclear and/or metabolic functions.The role of mutant Shp-2 in different cellular compartments may reveal novel avenues for understanding the diverse molecular mechanisms underpinning the aberrant activation of the myeloid transcriptional program in Ptpn11-mutated stem cells.Considering the distinctive subcellular localization of Shp-2 compared to other oncoproteins associated with JMML, it is important to ascertain whether dysregulated innate immune responses are also implicated in other JMML subtypes.
Another noteworthy nding of this study is the dysregulation of ribosomal biogenesis and function in Ptpn11 E76K/+ leukemic cells consistently throughout all stages, including leukemia-initiating stem cells.
Several ribosomal small and large subunit proteins displayed upregulation in Ptpn11 E76K/+ leukemic cells, consistent with the elevated protein translation essential for robust tumor cell growth.Intriguingly, there was a simultaneous decrease in the expression of certain ribosomal proteins.Recent research has revealed the heterogeneity of ribosomes, with different ribosome types displaying preferences for translating speci c subsets of mRNAs 22,23 .Diminished expression of ribosomal proteins has the potential to disrupt ribosome formation and function.This can also contribute to malignancies through several mechanisms.The impairment in ribosomes can impact the synthesis of crucial regulatory proteins involved in cell growth, differentiation, and maturation, such as the tumor suppressor p53 [42][43][44] .Moreover, reduced expression of speci c ribosomal proteins and perturbed ribosome function can induce chronic ribosomal stress, triggering cellular dysfunctions and genomic instability 22 .However, the precise mechanisms by which the Ptpn11 mutation selectively interferes with the expression of different ribosomal genes remain unclear.
In summary, our ndings reveal previously unappreciated mechanisms in the initial phase of JMML leukemogenesis, where leukemia-initiating mutant stem cells exploit innate immune signaling to gain a selective advantage and evade anti-tumor immunity.The signi cant dysregulation of proin ammatory proteins S100a9 and S100a8 underscores their pivotal role in orchestrating immune evasion and creating an in ammatory microenvironment conducive to leukemic progression.This insight offers new perspectives for developing therapeutic strategies to disrupt leukemia-initiating stem cells and improve treatment outcomes in JMML.

MATERIALS AND METHODS
Mice.Ptpn11 E76K Neo/+ conditional knock-in mice were generated in our previous study 6 .Mx1-Cre + (Strain #: 003556) 45 , mTmG dual-uorescent reporter transgenic mice (Strain #: 007676) 33 , C57BL/6 mice (CD45.2+ ) (Strain #: 000664), and BoyJ mice (CD45.1 + ) (Strain #: 002014) were purchased from the Jackson Laboratory.All mice were kept under speci c-pathogen-free conditions at Emory University Division of Animal Resources.Animal procedures complied with the NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee.
Patient specimens.De-identi ed samples from PTPN11-mutated patients with JMML and pediatric healthy controls normal BM biopsies were obtained from the University of California, San Francisco and the A ac Cancer and Blood Disorders Center Biorepository of Children's Healthcare of Atlanta.Samples were obtained after written, informed consent under locally approved institutional review board research protocols and in accordance with the Declaration of Helsinki.
Single-cell transcriptome pro ling.Fresh BM cells were collected and pooled from Ptpn11 E76K/+ /Mx1-Cre + mice and Ptpn11 +/+ /Mx1-Cre + control mice (3 mice/group), followed by the execution of the recommended protocol for the scRNA-seq 10x Genomics platform using v3 chemistry.In brief, scRNAseq raw reads were obtained following the standard protocol for Chromium Single Cell 3 Reagent Kits v3.Subsequently, the CellRanger 1 software from 10x Genomics was employed to identify celldiscriminating barcode sequence markers and unique molecular identi er (UMI) markers for different mRNA molecules within each cell.This process aimed to quantify the high-throughput single-cell transcriptome and conduct data quality statistics and comparisons against the original genome.Next, the Seurat 2 software package was utilized for further quality control (QC) and processing of the CellRanger results.In the QC step, delocalized cells were ltered by tting a generalized linear model.Subsequently, the distribution of nUMI (unique molecular identi er counts), nGene (number of detected genes), and percent.mito(percentage of mitochondrial genes) was assessed to lter out low-quality cells, such as double cells, multiple cells, or dead cells, leaving only quali ed cells for further bioinformatics analyses.t-distributed stochastic neighbor embedding (t-SNE) visualization and cell identi cation.The single-cell transcriptome underwent principal component analysis (PCA) for linear dimensionality reduction.Subsequently, the PCA results were visualized in a two-dimensional space using t-SNE, a non-linear dimensionality reduction technique.The Seurat platform's FindAllMarkers function was employed to identify marker genes for each cell classi cation relative to other cell populations.These identi ed genes serve as potential markers for each cell type.Visualization of the identi ed marker genes was carried out using the VlnPlot and FeaturePlot functions.Following the clustering process, the Single R platform was utilized to assign cell types based on published datasets 19,20 , thereby enhancing the accuracy of cell type classi cation.
Gene set enrichment analysis (GSEA).GSEA was conducted to identify genes associated with speci ed cell types such as HSCs (Hematopoietic Stem Cells) and GMPs (Granulocyte-Macrophage Progenitors).The analysis utilized the GSEA platform available at http://www.broadinstitute.org/gsea/index.jsp.To prepare input data for GSEA, the top 5000 variable genes in each group were selected using the Seurat "FindVariableGenes" function.Gene sets, including those from KEGG pathways and Gene Ontology (GO), were obtained from the molecular signatures database (MSigDB).
Single-cell regulatory network inference and clustering (SCENIC) analysis.SCENIC analyses were performed using version 1.1.2.2, corresponding to RcisTarget 1.2.1 and AUCell 1.4.1.The motifs database for RcisTarget and GRNboost was utilized with default parameters.In detail, the analysis involved identifying over-represented transcription factor binding motifs on a given gene list using the RcisTarget package.Subsequently, the AUCell package was employed to score the activity of each group of regulons in each cell.This process enabled the inference and clustering of regulatory networks at the single-cell level, offering insights into the regulatory landscape of the analyzed cell populations.
To evaluate the cell type speci city of each predicted regulon, the regulon speci city score (RSS) was computed, employing the Jensen-Shannon divergence (JSD) as a measure of similarity between two probability distributions.Speci cally, the JSD was calculated for each vector of binary regulon activity overlaps with the assignment of cells to speci c cell types.The connection speci city index (CSI) for all regulons was determined using the scFunctions package, accessible at https://github.com/FloWuenne/scFunctions/.Pseudotime analysis.We utilized the Monocle2 package (v2.9.0) for inferring cell differentiation trajectories.The speci c steps were as follows: First, we employed the importCDS function from the Monocle2 package to convert the Seurat object to the CellDataSet object.Next, the differentialGeneTest function was utilized to lter out ordering genes (genes with a q-value < 0.01).Then, we used the reduceDimension function to perform dimensionality reduction clustering.Finally, we applied the orderCells function to infer the differentiation trajectory.
Cell-cell communication analysis.We utilized CellPhoneDB (v2.0) to identify biologically relevant ligandreceptor interactions from single-cell transcriptomic data.We de ned a ligand or receptor as 'expressed' in a particular cell type if 10% of the cells of that type exhibited non-zero read counts for the ligand/receptor encoding gene.Statistical signi cance was assessed by randomly shu ing the cluster labels of all cells and repeating the aforementioned steps, thereby generating a null distribution for each ligand-receptor (LR) pair in each pairwise comparison between two cell types.Following 1,000 permutations, p-values were calculated using the normal distribution curve generated from the permuted LR pair interaction scores.To delineate networks of cell-cell communication, we connected any two cell types where the ligand was expressed in the former cell type and the receptor in the latter.The R package circlize was employed for visualizing the cell-cell communication networks.

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